Search for a command to run...
The Himalayan river Basins frequently experience devastating floods. These river basins require accurate predictions and timely warnings to support effective flood risk management. While accurate prediction is crucial for saving lives, disaster managers often face a difficult trade-off between computational cost and warning lead time. High-fidelity physics-based models are precise but are computationally expensive for rapid decision-making, whereas low-fidelity geo-spatial models often lack accuracy in data-scarce regions. Our proposal is a framework to improve the flood inundation prediction in the Himalayan basin by combining the reliability of hydrodynamic modeling with the speed of machine learning.In this study, we developed a 2D HEC-RAS model using a Rain-on-Grid approach to simulate the historical floods. We utilize the developed hydrodynamic model to generate a dataset of flood inundations that captures the basin's flow dynamics. These datasets will serve as the foundation for training advanced machine learning algorithms, including a Random Forest Regressor (RF) and a Convolutional Neural Network (CNN), to identify and predict flood patterns. Our model will integrate critical landscape features, including elevation, slope, land-use characteristics, the Normalized Difference Vegetation Index (NDVI), and satellite-derived rainfall data, to approximate the complex physical processes embedded in the hydrodynamic model. This allows the machine learning approach to achieve comparable predictive accuracy while reducing computational time. Through comprehensive validation against established benchmarks and real-world flood events, our research aims to deliver a scalable, computationally efficient, and highly accurate flood prediction tool. This framework has the potential to transform disaster preparedness and response capabilities in the Himalayan region by enabling timely, data-driven policy planning and proactive risk mitigation strategies.